Jetson Orin Nano Setup and Compatibility Issues

Issue Overview

Users are experiencing difficulties setting up and working with the Nvidia Jetson Orin Nano, particularly when transitioning from older Jetson Nano models. The main challenges include:

1. Compatibility issues between JetPack 4.x (used for older Jetson Nano) and JetPack 5.x (required for Jetson Orin Nano).
2. Problems installing and importing the jetcam module within Docker containers.
3. Lack of up-to-date tutorials and resources specifically tailored for Jetson Orin Nano.
4. Difficulties in running existing notebooks and examples designed for older Jetson models.

These issues are impacting users’ ability to get started with their Jetson Orin Nano devices and utilize them for AI and machine learning projects.

Possible Causes

1. Software version mismatch: JetPack 5.x introduces changes that may not be backward compatible with JetPack 4.x.
2. Outdated dependencies: Some libraries and modules, like jetcam, haven’t been updated in several years and may not be fully compatible with newer Jetson hardware or software.
3. Installation path issues: The jetcam module may be installed in a location not included in the Python path.
4. Docker container configuration: The default Docker container may not have the correct environment setup for all required modules.
5. Lack of documentation: Limited resources specifically for Jetson Orin Nano may lead to confusion when following older tutorials.

Troubleshooting Steps, Solutions & Fixes

1. Use the correct JetPack version:

   • Ensure you are using JetPack 5.x for Jetson Orin Nano.
   • Be aware that tutorials and examples for JetPack 4.x may need adaptation.

2. Install jetcam correctly:
   Follow these steps to properly install jetcam within the Docker container:

   cd /opt
   git clone https://github.com/NVIDIA-AI-IOT/jetcam
   cd jetcam
   python3 setup.py bdist_wheel
   pip3 install dist/jetcam*.whl
   cd /
   pip3 show jetcam
   python3 -m 'import jetcam'
   

3. Use the appropriate Docker container:

   • Run the container with the following command:

     sudo docker run --runtime nvidia -it --rm --device /dev/video0 nvcr.io/nvidia/l4t-ml:r35.2.1-py3
     

   • Add volume and network mapping as needed.

4. Check Python path:

   • Verify that the installation path is included in PYTHONPATH.
   • If not, add it manually or use the wheel installation method described above.

5. Explore alternative resources:

   • Try the Jetson Generative AI Playground tutorials, which are updated for JetPack 5: https://www.nvidia.com/en-us/ai-on-jetson/jetson-generative-ai/
   • Consider using the jetson-inference repository, which has been updated for newer Jetson models: https://github.com/dusty-nv/jetson-containers

6. Adapt existing tutorials:

   • When following tutorials designed for older Jetson models, be prepared to make adjustments for JetPack 5.x compatibility.
   • Use the l4t-ml container instead of the DLI container for JetPack 5.

7. Stay updated:

   • Regularly check for updates to key libraries and tools.
   • Be aware that some older packages like jetcam may have limited functionality or require updates.

8. Community support:

   • Engage with the Jetson community forums and GitHub discussions for the latest information and troubleshooting tips.
   • Report issues and contribute to open-source projects to help improve compatibility and documentation for Jetson Orin Nano.

By following these steps and being aware of the differences between Jetson Nano and Jetson Orin Nano, users should be able to overcome most initial setup challenges and begin working with their devices effectively.